



"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__=False						)      ->   List[str]:


		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
				    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
				    " instructions."						)
				raise

		if not is_sharded:
				a_  :   str			    =  os.path.abspath(lowerCAmelCase__						)
				logger.info(F"""Loading PyTorch weights from {pt_path}"""						)

				a_  :   Optional[Any]			    =  torch.load(lowerCAmelCase__,			map_location="cpu"						)
				logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters."""						)

				a_  :   Tuple			    =  convert_pytorch_state_dict_to_flax(lowerCAmelCase__,			lowerCAmelCase__						)
		else:
				# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
				a_  :   Any			    =  convert_pytorch_sharded_state_dict_to_flax(lowerCAmelCase__,			lowerCAmelCase__						)
		return flax_state_dict



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			)      ->   Tuple:


		def is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__						) -> bool:
				return len(set(lowerCAmelCase__						) & {key, (model_prefix,) + key}						) > 0

		# layer norm
		a_  :   Optional[Any]			    =  pt_tuple_key[:-1] + ("""scale""",)
		if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				return renamed_pt_tuple_key, pt_tensor

		# batch norm layer mean
		a_  :   Optional[int]			    =  pt_tuple_key[:-1] + ("""mean""",)
		if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				return renamed_pt_tuple_key, pt_tensor

		# batch norm layer var
		a_  :   str			    =  pt_tuple_key[:-1] + ("""var""",)
		if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				return renamed_pt_tuple_key, pt_tensor

		# embedding
		a_  :   Optional[int]			    =  pt_tuple_key[:-1] + ("""embedding""",)
		if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				return renamed_pt_tuple_key, pt_tensor

		# conv layer
		a_  :   Optional[int]			    =  pt_tuple_key[:-1] + ("""kernel""",)
		if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				a_  :   Union[str, Any]			    =  pt_tensor.transpose(2,			3,			1,			0						)
				return renamed_pt_tuple_key, pt_tensor

		# linear layer
		a_  :   Tuple			    =  pt_tuple_key[:-1] + ("""kernel""",)
		if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__						):
				a_  :   int			    =  pt_tensor.T
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm weight
		a_  :   int			    =  pt_tuple_key[:-1] + ("""weight""",)
		if pt_tuple_key[-1] == "gamma":
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm bias
		a_  :   List[Any]			    =  pt_tuple_key[:-1] + ("""bias""",)
		if pt_tuple_key[-1] == "beta":
				return renamed_pt_tuple_key, pt_tensor

		# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
		a_  :   Optional[Any]			    =  None
		if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
				a_  :   Any			    =  pt_tuple_key[-2] + """_g"""
		elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
				a_  :   List[Any]			    =  pt_tuple_key[-2] + """_v"""
		if name is not None:
				a_  :   int			    =  pt_tuple_key[:-3] + (name,)
				return renamed_pt_tuple_key, pt_tensor

		return pt_tuple_key, pt_tensor



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   List[Any]:
		a_  :   Optional[int]			    =  {k: v.numpy() for k, v in pt_state_dict.items()}

		a_  :   List[Any]			    =  flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers
		if "params" in flax_model.params:
				a_  :   Optional[Any]			    =  flax_model.params["""params"""]
		else:
				a_  :   Union[str, Any]			    =  flax_model.params
		a_  :   List[Any]			    =  flatten_dict(lowerCAmelCase__						)

		# add batch_stats keys,values to dict
		if "batch_stats" in flax_model.params:
				a_  :   List[str]			    =  flatten_dict(flax_model.params["batch_stats"]						)
				random_flax_state_dict.update(lowerCAmelCase__						)

		a_  :   Optional[int]			    =  {}

		a_  :   Any			    =  (model_prefix not in flax_model_params) and (
		    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
		)
		a_  :   Tuple			    =  (model_prefix in flax_model_params) and (
		    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
		)

		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
				a_  :   Any			    =  tuple(pt_key.split("."						)						)

				# remove base model prefix if necessary
				a_  :   Dict			    =  pt_tuple_key[0] == model_prefix
				if load_model_with_head_into_base_model and has_base_model_prefix:
						a_  :   Optional[Any]			    =  pt_tuple_key[1:]

				# Correctly rename weight parameters
				a_  :   int			    =  rename_key_and_reshape_tensor(
				    lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__						)

				# add model prefix if necessary
				a_  :   Optional[int]			    =  (model_prefix,) + flax_key in random_flax_state_dict
				if load_base_model_into_model_with_head and require_base_model_prefix:
						a_  :   Dict			    =  (model_prefix,) + flax_key

				if flax_key in random_flax_state_dict:
						if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
								raise ValueError(
								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # add batch stats if the model contains batchnorm layers
				if "batch_stats" in flax_model.params:
						if "mean" in flax_key[-1] or "var" in flax_key[-1]:
								a_  :   Optional[int]			    =  jnp.asarray(lowerCAmelCase__						)
								continue
						# remove num_batches_tracked key
						if "num_batches_tracked" in flax_key[-1]:
								flax_state_dict.pop(lowerCAmelCase__,			lowerCAmelCase__						)
								continue

						# also add unexpected weight so that warning is thrown
						a_  :   Dict			    =  jnp.asarray(lowerCAmelCase__						)

				else:
						# also add unexpected weight so that warning is thrown
						a_  :   List[str]			    =  jnp.asarray(lowerCAmelCase__						)

		return unflatten_dict(lowerCAmelCase__						)



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   Dict:
		import torch

		# Load the index
		a_  :   Optional[Any]			    =  {}
		for shard_file in shard_filenames:
				# load using msgpack utils
				a_  :   int			    =  torch.load(lowerCAmelCase__						)
				a_  :   str			    =  {k: v.numpy() for k, v in pt_state_dict.items()}

				a_  :   Any			    =  flax_model.base_model_prefix

				# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
				if "batch_stats" in flax_model.params:
						a_  :   int			    =  flax_model.params["""params"""]

						a_  :   List[Any]			    =  flatten_dict(lowerCAmelCase__						)
						random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]						)						)
				else:
						a_  :   Tuple			    =  flax_model.params
						a_  :   Optional[Any]			    =  flatten_dict(lowerCAmelCase__						)

				a_  :   List[Any]			    =  (model_prefix not in flax_model_params) and (
				    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
				)
				a_  :   Optional[int]			    =  (model_prefix in flax_model_params) and (
				    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
				)
				# Need to change some parameters name to match Flax names
				for pt_key, pt_tensor in pt_state_dict.items():
						a_  :   Union[str, Any]			    =  tuple(pt_key.split("."						)						)

						# remove base model prefix if necessary
						a_  :   Tuple			    =  pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
								a_  :   str			    =  pt_tuple_key[1:]

						# Correctly rename weight parameters
						a_  :   Dict			    =  rename_key_and_reshape_tensor(
						    lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__						)
						# add model prefix if necessary
						a_  :   int			    =  (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
								a_  :   str			    =  (model_prefix,) + flax_key

						if flax_key in random_flax_state_dict:
								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
										raise ValueError(
										    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
										    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

            # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
								if "mean" in flax_key[-1]:
										a_  :   List[Any]			    =  jnp.asarray(lowerCAmelCase__						)
										continue
								if "var" in flax_key[-1]:
										a_  :   Union[str, Any]			    =  jnp.asarray(lowerCAmelCase__						)
										continue
								# remove num_batches_tracked key
								if "num_batches_tracked" in flax_key[-1]:
										flax_state_dict.pop(lowerCAmelCase__,			lowerCAmelCase__						)
										continue

								# also add unexpected weight so that warning is thrown
								a_  :   Tuple			    =  jnp.asarray(lowerCAmelCase__						)

						else:
								# also add unexpected weight so that warning is thrown
								a_  :   Optional[Any]			    =  jnp.asarray(lowerCAmelCase__						)
		return unflatten_dict(lowerCAmelCase__						)



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   Optional[int]:
		a_  :   Union[str, Any]			    =  os.path.abspath(lowerCAmelCase__						)
		logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""						)

		# import correct flax class
		a_  :   List[Any]			    =  getattr(lowerCAmelCase__,			"Flax" + model.__class__.__name__						)

		# load flax weight dict
		with open(lowerCAmelCase__,			"rb"						) as state_f:
				try:
						a_  :   Optional[int]			    =  from_bytes(lowerCAmelCase__,			state_f.read()						)
				except UnpicklingError:
						raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """						)

		return load_flax_weights_in_pytorch_model(lowerCAmelCase__,			lowerCAmelCase__						)



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   str:


		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
				    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
				    " instructions."						)
				raise

		# check if we have bf16 weights
		a_  :   str			    =  flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__						: x.dtype == jnp.bfloataa,			lowerCAmelCase__						)						).values()
		if any(lowerCAmelCase__						):
				# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
				    "before loading those in PyTorch model."						)
				a_  :   List[str]			    =  jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params,			lowerCAmelCase__						)

		a_  :   List[Any]			    =  flatten_dict(lowerCAmelCase__						)
		a_  :   List[Any]			    =  pt_model.state_dict()

		a_  :   Dict			    =  (pt_model.base_model_prefix in flax_state) and (
		    pt_model.base_model_prefix not in {k.split("."						)[0] for k in pt_model_dict.keys()}
		)
		a_  :   Union[str, Any]			    =  (pt_model.base_model_prefix not in flax_state) and (
		    pt_model.base_model_prefix in {k.split("."						)[0] for k in pt_model_dict.keys()}
		)

		# keep track of unexpected & missing keys
		a_  :   List[str]			    =  []
		a_  :   Optional[Any]			    =  set(pt_model_dict.keys()						)

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				a_  :   str			    =  flax_key_tuple[0] == pt_model.base_model_prefix
				a_  :   Optional[int]			    =  """.""".join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

				# adapt flax_key to prepare for loading from/to base model only
				if load_model_with_head_into_base_model and has_base_model_prefix:
						a_  :   Optional[Any]			    =  flax_key_tuple[1:]
				elif load_base_model_into_model_with_head and require_base_model_prefix:
						a_  :   Optional[Any]			    =  (pt_model.base_model_prefix,) + flax_key_tuple

				# rename flax weights to PyTorch format
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCAmelCase__						) not in pt_model_dict:
						# conv layer
						a_  :   Optional[Any]			    =  flax_key_tuple[:-1] + ("""weight""",)
						a_  :   List[str]			    =  jnp.transpose(lowerCAmelCase__,			(3, 2, 0, 1)						)
				elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__						) not in pt_model_dict:
						# linear layer
						a_  :   Tuple			    =  flax_key_tuple[:-1] + ("""weight""",)
						a_  :   Union[str, Any]			    =  flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
						a_  :   Optional[int]			    =  flax_key_tuple[:-1] + ("""weight""",)

				# adding batch stats from flax batch norm to pt
				elif "mean" in flax_key_tuple[-1]:
						a_  :   Dict			    =  flax_key_tuple[:-1] + ("""running_mean""",)
				elif "var" in flax_key_tuple[-1]:
						a_  :   Union[str, Any]			    =  flax_key_tuple[:-1] + ("""running_var""",)

				if "batch_stats" in flax_state:
						a_  :   Optional[Any]			    =  """.""".join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
				else:
						a_  :   Optional[Any]			    =  """.""".join(lowerCAmelCase__						)

				# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
				a_  :   str			    =  {}
				# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
				for key in pt_model_dict:
						a_  :   Tuple			    =  key.split("."						)
						a_  :   str			    =  None
						if key_components[-3::2] == ["parametrizations", "original0"]:
								a_  :   int			    =  key_components[-2] + """_g"""
						elif key_components[-3::2] == ["parametrizations", "original1"]:
								a_  :   List[str]			    =  key_components[-2] + """_v"""
						if name is not None:
								a_  :   Dict			    =  key_components[:-3] + [name]
								a_  :   List[Any]			    =  """.""".join(lowerCAmelCase__						)
								a_  :   Union[str, Any]			    =  key

				if flax_key in special_pt_names:
						a_  :   int			    =  special_pt_names[flax_key]

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
								    F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)
						else:
								# add weight to pytorch dict
								a_  :   Union[str, Any]			    =  np.asarray(lowerCAmelCase__						) if not isinstance(lowerCAmelCase__,			np.ndarray						) else flax_tensor
								a_  :   Optional[Any]			    =  torch.from_numpy(lowerCAmelCase__						)
								# remove from missing keys
								missing_keys.remove(lowerCAmelCase__						)
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(lowerCAmelCase__						)

		pt_model.load_state_dict(lowerCAmelCase__						)

		# re-transform missing_keys to list
		a_  :   int			    =  list(lowerCAmelCase__						)

		if len(lowerCAmelCase__						) > 0:
				logger.warning(
				    "Some weights of the Flax model were not used when initializing the PyTorch model"
				    F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
				    F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
				    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
				    F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
				    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
				    " FlaxBertForSequenceClassification model)."						)
		else:
				logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""						)
		if len(lowerCAmelCase__						) > 0:
				logger.warning(
				    F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
				    F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
				    " use it for predictions and inference."						)
		else:
				logger.warning(
				    F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
				    "If your task is similar to the task the model of the checkpoint was trained on, "
				    F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""						)

		return pt_model




import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






